Matplotlib 3.0 Cookbook

How to do it...

The following are the steps to implement the required logic:

1. Define the figure and axes:

fig, ax = plt.subplots()

2. Prepare the data for the graph to be plotted, sigmoid in this case, and plot it:

x = np.arange(-10, 10, 0.01)y = 1 / (1 + np.exp(-x))line, = ax.plot(x, y)

3. Define the initialization function, init():

def init():     line.set_ydata([np.nan] * len(x))    return line

4. Define the animation function, animate():

def animate(i):    line.set_ydata(1 / (1 + np.exp(-(x+i/100)))) # keep refreshing frame by frame    return line,

5. Activate the animation:

ani = FuncAnimation(fig, animate, 1000, init_func=init, blit=True,                     interval=2, save_count=50,                     repeat=False, repeat_delay=1000)

6. Send the output to the specified backend: